{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize']=(15,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in power\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "x1=np.linspace(-1,1,100)\n",
    "y1=uniform.pdf(x1,loc=-1,scale=2)\n",
    "x2=np.linspace(0,1,100)\n",
    "y2=np.power(x2,-0.5)/2\n",
    "rand_x = uniform.rvs(size=10000)\n",
    "y3=np.power(rand_x,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'(c)')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=plt.subplot(131)\n",
    "ax1.plot(x1,y1)\n",
    "ax1.set_xlabel('(a)')\n",
    "ax2=plt.subplot(132)\n",
    "ax2.plot(x2,y2)\n",
    "ax2.set_xlabel('(b)')\n",
    "ax3=plt.subplot(133)\n",
    "bins=np.linspace(0,1,100)\n",
    "result=ax3.hist(y3,bins,align='left',rwidth=0.7,density=True)\n",
    "ax3.set_xlabel('(c)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
